Phosphoproteome Profiling of Transforming Growth Factor (TGF)-β Signaling: Abrogation of TGFβ1-dependent Phosphorylation of Transcription Factor-II-I (TFII-I) Enhances Cooperation of TFII-I and Smad3 in Transcription

Transforming growth factor-β (TGFβ) signaling involves activation of a number of signaling pathways, several of which are controlled by phosphorylation events. Here, we describe a phosphoproteome profiling of MCF-7 human breast epithelial cells treated with TGFβ1. We identified 32 proteins that change their phosphorylation upon treatment with TGFβ1; 26 of these proteins are novel targets of TGFβ1. We show that Smad2 and Smad3 have different effects on the dynamics of TGFβ1-induced protein phosphorylation. The identified proteins belong to nine functional groups, e.g., proteins regulating RNA processing, cytoskeletal rearrangements, and proteasomal degradation. To evaluate the proteomics findings, we explored the functional importance of TGFβ1-dependent phosphorylation of one of the targets, i.e., transcription factor-II-I (TFII-I). We confirmed that TGFβ1 stimulated TFII-I phosphorylation at serine residues 371 and 743. Abrogation of the phosphorylation by replacement of Ser371 and Ser743 with alanine residues resulted in enhanced complex formation between TFII-I and Smad3, and enhanced cooperation between TFII-I and Smad3 in transcriptional regulation, as evaluated by a microarray-based measurement of expression of endogenous cyclin D2, cyclin D3, and E2F2 genes, and by a luciferase reporter assay. Thus, TGFβ1-dependent phosphorylation of TFII-I may modulate TGFβ signaling at the transcriptional level

Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells

Protein contents in platelets are frequently changed upon tumor development and metastasis. However, how cancer cells can influence protein-selective redistribution and release within platelets, thereby promoting tumor development, remains largely elusive. With fluorescence-based super-resolution stimulated emission depletion (STED) imaging we reveal how specific proteins, implicated in tumor progression and metastasis, re-distribute within platelets, when subject to soluble activators (thrombin, adenosine diphosphate and thromboxane A2), and when incubated with cancer (MCF-7, MDA-MB-231, EFO21) or non-cancer cells (184A1, MCF10A). Upon cancer cell incubation, the cell-adhesion protein P-selectin was found to re-distribute into circular nano-structures, consistent with accumulation into the membrane of protein-storing alpha-granules within the platelets. These changes were to a significantly lesser extent, if at all, found in platelets incubated with normal cells, or in platelets subject to soluble platelet activators. From these patterns, we developed a classification procedure, whereby platelets exposed to cancer cells, to non-cancer cells, soluble activators, as well as non-activated platelets all could be identified in an automatic, objective manner. We demonstrate that STED imaging, in contrast to electron and confocal microscopy, has the necessary spatial resolution and labelling efficiency to identify protein distribution patterns in platelets and can resolve how they specifically change upon different activations. Combined with image analyses of specific protein distribution patterns within the platelets, STED imaging can thus have a role in future platelet-based cancer diagnostics and therapeutic monitoring. The presented approach can also bring further clarity into fundamental mechanisms for cancer cell-platelet interactions, and into non-contact cell-to-cell interactions in general

Peritoneal cancer index predicts severe complications after ovarian cancer surgery

INTRODUCTION: prediction and importance of severe postoperative complications after ovarian cancer surgery is a strong issue in patient selection and evaluation. Pre- and early peroperative predictors of severe 30-days postoperative complications (Clavien-Dindo class ≥3) after surgery for primary ovarian cancer are not fully established, neither their impact on patients' survival. MATERIALS AND METHODS: A prospective observational study included 256 patients with primary ovarian cancer FIGO stages IIB-IV, operated during 2009-2018 in a primary or interval debulking surgery setting. Patient variables were analysed in relation to severe postoperative complications (Clavien-Dindo class ≥3) and overall survival. RESULTS: High-grade postoperative complications occurred in 24.2% patients. Class 3a complications were observed in 12.5% cases. High-grade complications class ≥3 were observed in 31.6% after primary debulking surgery compared to 12.2% after interval debulking surgery (p = 0.0004). Peritoneal cancer index ≥21 and preoperative albumin concentration ≤33 g/L were independent predictors of high-grade complications. Peritoneal cancer index correlated with the surgical complexity score and completeness of cytoreduction. Increased peritoneal cancer index was a negative predictor of overall survival, but high-grade complications did not influence survival negatively. CONCLUSIONS: Peritoneal cancer index ≥21 was an independent predictor of high-grade complications after ovarian cancer surgery. Increased peritoneal cancer index also impacted overall survival negatively, but high-grade complications did not influence overall survival

Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells

Protein contents in platelets are frequently changed upon tumor development and metastasis. However, how cancer cells can influence protein-selective redistribution and release within platelets, thereby promoting tumor development, remains largely elusive. With fluorescence-based super-resolution stimulated emission depletion (STED) imaging we reveal how specific proteins, implicated in tumor progression and metastasis, re-distribute within platelets, when subject to soluble activators (thrombin, adenosine-diphosphate and thromboxaneA2), and when incubated with cancer (MCF-7, MDA-MB-231, EFO21) or non-cancer cells (184A1, MCF10A). Upon cancer cell incubation, the cell-adhesion protein P-selectin was found to re-distribute into circular nano-structures, consistent with accumulation into the membrane of protein-storing alpha-granules within the platelets. These changes were to a significantly lesser extent, if at all, found in platelets incubated with normal cells, or in platelets subject to soluble platelet activators. From these patterns, we developed a classification procedure, whereby platelets exposed to cancer cells, to non-cancer cells, soluble activators as well as non-activated platelets all could be identified in an automatic, objective manner. We demonstrate that STED imaging, in contrast to electron and confocal microscopy, has the necessary spatial resolution and labelling efficiency to identify protein distribution patterns in platelets and can resolve how they specifically change upon different activations. Combined with image analyses of specific protein distribution patterns within the platelets, STED imaging can thus have a role in future platelet-based cancer diagnostics and therapeutic monitoring. The presented approach can also bring further clarity into fundamental mechanisms for cancer cell-platelet interactions, and into non-contact cell-to-cell interactions in general. QC 20190405</p

Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells

Protein contents in platelets are frequently changed upon tumor development and metastasis. However, how cancer cells can influence protein-selective redistribution and release within platelets, thereby promoting tumor development, remains largely elusive. With fluorescence-based super-resolution stimulated emission depletion (STED) imaging we reveal how specific proteins, implicated in tumor progression and metastasis, re-distribute within platelets, when subject to soluble activators (thrombin, adenosine-diphosphate and thromboxaneA2), and when incubated with cancer (MCF-7, MDA-MB-231, EFO21) or non-cancer cells (184A1, MCF10A). Upon cancer cell incubation, the cell-adhesion protein P-selectin was found to re-distribute into circular nano-structures, consistent with accumulation into the membrane of protein-storing alpha-granules within the platelets. These changes were to a significantly lesser extent, if at all, found in platelets incubated with normal cells, or in platelets subject to soluble platelet activators. From these patterns, we developed a classification procedure, whereby platelets exposed to cancer cells, to non-cancer cells, soluble activators as well as non-activated platelets all could be identified in an automatic, objective manner. We demonstrate that STED imaging, in contrast to electron and confocal microscopy, has the necessary spatial resolution and labelling efficiency to identify protein distribution patterns in platelets and can resolve how they specifically change upon different activations. Combined with image analyses of specific protein distribution patterns within the platelets, STED imaging can thus have a role in future platelet-based cancer diagnostics and therapeutic monitoring. The presented approach can also bring further clarity into fundamental mechanisms for cancer cell-platelet interactions, and into non-contact cell-to-cell interactions in general. QC 20190405</p

Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer.

BACKGROUND: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. METHODS: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). RESULTS: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. CONCLUSIONS: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer

Additional file 1: Table S1. of Platelet protein biomarker panel for ovarian cancer diagnosis

Detailed description of the clinical material. Table S2. Description of the primary antibodies. Table S3. Function of proteins biomarkers, relation to ovarian cancer and platelet function. Table S4. Predicted functional partners for identified biomarkers. (XLSX 32 kb